Learning to rank recommendations with the k-order statistic loss

Weston, Jason; Yee, Hector; Weiss, Ron

doi:10.1145/2507157.2507210

Cited by 48 publications

(41 citation statements)

References 8 publications

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“…Many other modifications to ranking loss functions have been proposed in the literature that interpolate between the the two first loss functions proposed above, or which prioritize correctly predicting the top ranked choices. These losses include the area under the curve loss [Ste07], ordered weighted average of pairwise classification losses [UBG09], the weighted approximate-rank pairwise loss [WBU10], the k-order statistic loss [WYW13], and the accuracy at the top loss [BCMR12].…”

Section: Examplesmentioning

confidence: 99%

Generalized Low Rank Models

Udell¹,

Horn²,

Zadeh³

et al. 2016

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Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, k-means, k-SVD, and maximum margin matrix factorization. The method handles heterogeneous data sets, and leads to coherent schemes for compressing, denoising, and imputing missing entries across all data types simultaneously. It also admits a number of interesting interpretations of the low rank factors, which allow clustering of examples or of features. We propose several parallel algorithms for fitting generalized low rank models, and describe implementations and numerical results.This manuscript is a draft. Comments sent to udell@stanford.edu are welcome.

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Section: Examplesmentioning

confidence: 99%

Generalized Low Rank Models

Udell¹,

Horn²,

Zadeh³

et al. 2016

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“…This can be done by training a rating-based model with matrix completion to learn from observed user-item associations (either explicit or implicit feedback) to predict associations that are unobserved [1,5,12,13,15,16,18,20,21,26,36]. In addition to this rating-based approach, ranking-based methods have been proposed based on optimizing ranking loss; the ranking-based methods [21,25,[33][34][35] have been found more suitable for implicit feedback. However, many existing model-based CF algorithms leverage only the user-item associations available in a given user-item bipartite graph.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Similarity Embedding for Recommender Systems

Chen

Wang

Tsai

et al. 2019

The World Wide Web Conference

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We present collaborative similarity embedding (CSE), a unified framework that exploits comprehensive collaborative relations available in a user-item bipartite graph for representation learning and recommendation. In the proposed framework, we differentiate two types of proximity relations: direct proximity and k-th order neighborhood proximity. While learning from the former exploits direct user-item associations observable from the graph, learning from the latter makes use of implicit associations such as user-user similarities and item-item similarities, which can provide valuable information especially when the graph is sparse. Moreover, for improving scalability and flexibility, we propose a sampling technique that is specifically designed to capture the two types of proximity relations. Extensive experiments on eight benchmark datasets show that CSE yields significantly better performance than state-of-theart recommendation methods.

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“…It is argued that this approach uses more informative ( u , i , j ) triples and avoids wasteful gradient computations resulting in faster convergence. A related approach is to sample j from a random subset of irrelevant items that have higher ranking scores than i with a margin. Yet, in another related approach, j is sampled from a random but fixed‐sized subset,

S_{u}^{-}

, of irrelevant items for user u .…”

Section: Extensions To Pltr Algorithmsmentioning

confidence: 99%

Parallel pairwise learning to rank for collaborative filtering

Yağcı

Aytekin

Gürgen

2019

Concurrency and Computation

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Summary Pairwise learning to rank is known to be suitable for a wide range of collaborative filtering applications. In this work, we show that its efficiency can be greatly improved with parallel stochastic gradient descent schemes. Accordingly, we first propose to extrapolate two such state‐of‐the‐art schemes to the pairwise learning to rank problem setting. We then show the versatility of these proposals by showing the applicability of several important extensions commonly desired in practice. Theoretical as well as extensive empirical analyses of our proposals show remarkable efficiency results for pairwise learning to rank in offline and stream learning settings.

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Learning to rank recommendations with the k-order statistic loss

Cited by 48 publications

References 8 publications

Generalized Low Rank Models

Generalized Low Rank Models

Collaborative Similarity Embedding for Recommender Systems

Parallel pairwise learning to rank for collaborative filtering

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